Title :
Benchmarking performance of massively parallel AI architectures
Author :
DeMara, Ronald F. ; Kitano, Hiroaki
Author_Institution :
Univ. of Southern California, Los Angeles, CA, USA
Abstract :
The authors address the architectural evaluation of massively parallel machines suitable for artificial intelligence (AI). The approach is to identify the impact of specific algorithm features by measuring execution time on a SNAP-1 and a Connection Machine-2 using different knowledge base and machine configurations. Since a wide variety of parallel AI languages and processing architectures are in use, the authors developed a portable benchmark set for Parallel AI Computational Efficiency (PACE). PACE provides a representative set of processing workloads, knowledge base topologies, and performance indices. The authors also analyze speedup and scalability of fundamental AI operations in terms of the massively parallel paradigm
Keywords :
artificial intelligence; knowledge based systems; parallel architectures; parallel processing; performance evaluation; Connection Machine-2; Parallel AI Computational Efficiency; SNAP-1; architectural evaluation; benchmarking performance; execution time; knowledge base; machine configurations; massively parallel AI architectures; parallel languages; portable benchmark set; scalability; speedup; Artificial intelligence; Computer architecture; Computer languages; Computer science; Kernel; Parallel machines; Parallel processing; Speech analysis; Time measurement; Topology;
Conference_Titel :
Frontiers of Massively Parallel Computation, 1992., Fourth Symposium on the
Conference_Location :
McLean, VA
Print_ISBN :
0-8186-2772-7
DOI :
10.1109/FMPC.1992.234865
